Generating Equations with Genetic Programming for Control of a Movable Inverted Pendulum

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Shimooka:1998:geGPcmip,
  author =       "Hiroaki Shimooka and Yoshiji Fujimoto",
  title =        "Generating Equations with Genetic Programming for
                 Control of a Movable Inverted Pendulum",
  booktitle =    "Simulated Evolution and Learning: Second Asia-Pacific
                 Conference on Simulated Evolution and Learning,
                 SEAL'98. Selected Papers",
  year =         "1998",
  editor =       "R. I. Bob McKay and X. Yao and Charles S. Newton and 
                 J.-H. Kim and T. Furuhashi",
  volume =       "1585",
  series =       "LNAI",
  pages =        "179--186",
  address =      "Australian Defence Force Academy, Canberra,
                 Australia",
  publisher_address = "Heidelberg",
  month =        "24-27 " # nov,
  publisher =    "Springer-Verlag",
  note =         "published in 1999",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0302-9743",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1585&spage=179",
  abstract =     "Equations for calculating the control force of a
                 movable inverted pendulum are generated directly with
                 Genetic Programming (GP). The task of a movable
                 inverted pendulum is to control the force given a cart
                 on which a pole is hinged, not only to keep a pole
                 standing but also to move it to an arbitrary target
                 position. As the results of experiments, intelligent
                 control equations are obtained that can lean the pole
                 toward a target position by pulling the cart in the
                 opposite direction, and then move the cart to the
                 target while keeping the pole standing inversely. They
                 also have the robustness to move the cart with the pole
                 standing to the new target position when the target is
                 changed, even if the cart is moving to the old target
                 position. The robustness of the problem is
                 experimentally defined and the appropriate value of the
                 parsimony factor in GP is identified to obtain control
                 equations with robustness and simplicity as the
                 solutions.",
  notes =        "SEAL'98 Published as springer-verlag LNAI 1585
                 SEAL98#AP11

                 A1 Department of Applied Mathematics and Informatics
                 Faculty of Science and Technology, Ryukoku University
                 1-5 Yokoya, Ooe, Seta, Ohtsu, Shiga 520-2194 Japan
                 fujimoto@math.ryukoku.ac.jp",
}

Genetic Programming entries for Hiroaki Shimooka Yoshiji Fujimoto

Citations